{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['stepsize-cross-entropy', 'lambda-baseline', 'stepsize-sigma', 'degree-polynomial', 'stepsize-mu', 'seed', 'lambda-center'])\n"
     ]
    }
   ],
   "source": [
    "import os, json, numpy as np\n",
    "\n",
    "json_filename = 'poly_v_clone_results.json'\n",
    "with open(json_filename, 'r') as f:\n",
    "    results = json.load(f)\n",
    "\n",
    "trials = results['trials']\n",
    "trials = [trial for trial in trials if trial.get(\"finalMetric\", None) is not None]\n",
    "\n",
    "hyperparameter_keys = trials[0]['hyperparameters'].keys()\n",
    "\n",
    "print(hyperparameter_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hyperparam_values = {\n",
    "\n",
    "}\n",
    "ber_values = []\n",
    "condition_key = 'degree-polynomial'\n",
    "# condition_key = None\n",
    "condition_upper = 2\n",
    "condition_lower = 2\n",
    "for trial in trials:\n",
    "    if condition_key is not None:\n",
    "        if not  condition_lower <= float(trial['hyperparameters'][condition_key]) <= condition_upper:\n",
    "            continue\n",
    "    for hyperparameter in hyperparameter_keys:\n",
    "        hvals = hyperparam_values.get(hyperparameter, [])\n",
    "        hvals += [trial['hyperparameters'][hyperparameter]]\n",
    "        hyperparam_values[hyperparameter] = hvals\n",
    "    ber_values += [trial['finalMetric']['objectiveValue']]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stepsize-cross-entropy 0.019573778254241437 0.019573778254241437\n",
      "lambda-baseline 0.005187850945012679 0.005187850945012679\n",
      "stepsize-sigma 0.0031447427414969344 0.0031447427414969344\n",
      "degree-polynomial 2.083333333333333 2.083333333333333\n",
      "stepsize-mu 0.293452721226949 0.293452721226949\n",
      "lambda-center 0.08424570310506738 0.08424570310506738\n"
     ]
    },
    {
     "data": {
      "image/png": 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kLsnc/Pz8mLuTJC1n5EBP8lxgW1VdvtJyVbWpqmaranZmZmbU3UmSVjHOGfovAc9Pcivw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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f6f6e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ce1240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1106d2ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fe5cba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c6c1f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f824278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "ber_filter = .2\n",
    "for hyperparameter in hyperparam_values.keys():\n",
    "    if hyperparameter =='seed':\n",
    "        continue\n",
    "    x = []\n",
    "    fig, ax = plt.subplots()\n",
    "    for i, ber in enumerate(ber_values):\n",
    "        if ber <= ber_filter:\n",
    "            x += [float(hyperparam_values[hyperparameter][i])]\n",
    "    ax.set_title(hyperparameter)\n",
    "    bins = np.linspace(min(x), max(x))\n",
    "    n, bins, patches = ax.hist(x, bins=6, density=True)\n",
    "#     ax.xaxis.set_major_locator(plt.MultipleLocator(1))%%!\n",
    "#     print(bins)\n",
    "    print(hyperparameter, (bins[np.argmax(n)] + bins[np.argmax(n)+1])/2 ,(bins[np.argmax(n)] + bins[np.argmax(n)+1])/2)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "nbins = 20\n",
    "ber_filter = .15\n",
    "for hyperparameter in hyperparam_values.keys():\n",
    "    plt.title(hyperparameter)\n",
    "    paired_data = zip(hyperparam_values[hyperparameter], ber_values)\n",
    "    paired_data = sorted(paired_data, key = lambda d: d[0])\n",
    "    paired_data = [pair for pair in paired_data if pair[1] <= ber_filter]\n",
    "#     min_x = paired_data[0][0]\n",
    "#     max_x = paired_data[-1][0]\n",
    "#     range = max_x - min_x\n",
    "#     binsize = range / n_bins\n",
    "    x, y = list(zip(*paired_data))\n",
    "    plt.plot(x, y)\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
